The success of imaging spectrometry in mineralogic mapping of natural terrains indicates that the technology can also be used to assess the environmental impact of human activities in certain instances. Specifically, this paper describes an investigation into the use of data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for mapping the spread of, and assessing changes in, the mineralogic character of tailings from a major silver and base metal mining district. The area under investigation is the Coeur d'Alene River Valley in northern Idaho. Mining has been going on in and around the towns of Kellogg and Wallace, Idaho since the 1880's. In the Kellogg-Smelterville Flats area, west of Kellogg, mine tailings were piled alongside the South Fork of the Coeur d'Alene River. Until the construction of tailings ponds in 1968 much of these waste materials were washed directly into the South Fork. The Kellogg-Smelterville area was declared an Environmental Protection Agency (EPA) Superfund site in 1983 and remediation efforts are currently underway. Recent studies have demonstrated that sediments in the Coeur d'Alene River and in the northern part of Lake Coeur d'Alene, into which the river flows, are highly enriched in Ag, Cu, Pb, Zn, Cd, Hg, As, and Sb. These trace metals have become aggregated in iron oxide and oxyhydroxide minerals and/or mineraloids. Reflectance spectra of iron-rich tailing materials are shown. Also shown are spectra of hematite and goethite. The broad bandwidth and long band center (near 1 micron) of the Fe(3+) crystal-field band of the iron-rich sediment samples combined with the lack of features on the Fe(3+) -O(2-) charge transfer absorption edge indicates that the ferric oxide and/or oxyhydroxide in these sediments is poorly crystalline to amorphous in character. Similar features are seen in poorly crystalline basaltic weathering products (e.g., palagonites). The problem of mapping and analyzing the downriver occurrences of iron rich tailings in the Coeur d'Alene (CDA) River Valley using remotely sensed data is complicated by the full vegetation cover present in the area. Because exposures of rock and soil were sparse, the data processing techniques used in this study were sensitive to detecting materials at subpixel scales. The methods used included spectral mixture analysis and a constrained energy minimization technique.